Overview

Brought to you by YData

Dataset statistics

Number of variables28
Number of observations1,470
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory275.8 KiB
Average record size in memory192.1 B

Variable types

Numeric14
Categorical14

Alerts

Age is highly overall correlated with TotalWorkingYearsHigh correlation
Department is highly overall correlated with EducationField and 1 other fieldsHigh correlation
EducationField is highly overall correlated with DepartmentHigh correlation
JobLevel is highly overall correlated with JobRole and 2 other fieldsHigh correlation
JobRole is highly overall correlated with Department and 1 other fieldsHigh correlation
MonthlyIncome is highly overall correlated with JobLevel and 1 other fieldsHigh correlation
PercentSalaryHike is highly overall correlated with PerformanceRatingHigh correlation
PerformanceRating is highly overall correlated with PercentSalaryHikeHigh correlation
TotalWorkingYears is highly overall correlated with Age and 3 other fieldsHigh correlation
YearsAtCompany is highly overall correlated with TotalWorkingYears and 3 other fieldsHigh correlation
YearsInCurrentRole is highly overall correlated with YearsAtCompany and 2 other fieldsHigh correlation
YearsSinceLastPromotion is highly overall correlated with YearsAtCompany and 1 other fieldsHigh correlation
YearsWithCurrManager is highly overall correlated with YearsAtCompany and 1 other fieldsHigh correlation
EducationField has 27 (1.8%) zerosZeros
JobRole has 131 (8.9%) zerosZeros
NumCompaniesWorked has 197 (13.4%) zerosZeros
TrainingTimesLastYear has 54 (3.7%) zerosZeros
YearsAtCompany has 44 (3.0%) zerosZeros
YearsInCurrentRole has 244 (16.6%) zerosZeros
YearsSinceLastPromotion has 581 (39.5%) zerosZeros
YearsWithCurrManager has 263 (17.9%) zerosZeros
PTOs Utilized has 70 (4.8%) zerosZeros

Reproduction

Analysis started2025-10-06 09:38:52.218630
Analysis finished2025-10-06 09:39:04.969836
Duration12.75 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Age
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.92381
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-10-06T17:39:05.148361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.1353735
Coefficient of variation (CV)0.24741146
Kurtosis-0.40414514
Mean36.92381
Median Absolute Deviation (MAD)6
Skewness0.4132863
Sum54278
Variance83.455049
MonotonicityNot monotonic
2025-10-06T17:39:05.213533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3578
 
5.3%
3477
 
5.2%
3669
 
4.7%
3169
 
4.7%
2968
 
4.6%
3261
 
4.1%
3060
 
4.1%
3358
 
3.9%
3858
 
3.9%
4057
 
3.9%
Other values (33)815
55.4%
ValueCountFrequency (%)
188
 
0.5%
199
 
0.6%
2011
 
0.7%
2113
 
0.9%
2216
 
1.1%
2314
 
1.0%
2426
1.8%
2526
1.8%
2639
2.7%
2748
3.3%
ValueCountFrequency (%)
605
 
0.3%
5910
0.7%
5814
1.0%
574
 
0.3%
5614
1.0%
5522
1.5%
5418
1.2%
5319
1.3%
5218
1.2%
5119
1.3%

Attrition
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1233 
1
237 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1,470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01233
83.9%
1237
 
16.1%

Length

2025-10-06T17:39:05.279751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T17:39:05.346029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
01233
83.9%
1237
 
16.1%

Most occurring characters

ValueCountFrequency (%)
01233
83.9%
1237
 
16.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01233
83.9%
1237
 
16.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01233
83.9%
1237
 
16.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01233
83.9%
1237
 
16.1%

BusinessTravel
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2
1043 
1
277 
0
150 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1,470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
21043
71.0%
1277
 
18.8%
0150
 
10.2%

Length

2025-10-06T17:39:05.403462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T17:39:05.457849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
21043
71.0%
1277
 
18.8%
0150
 
10.2%

Most occurring characters

ValueCountFrequency (%)
21043
71.0%
1277
 
18.8%
0150
 
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
21043
71.0%
1277
 
18.8%
0150
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
21043
71.0%
1277
 
18.8%
0150
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
21043
71.0%
1277
 
18.8%
0150
 
10.2%

Department
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
961 
2
446 
0
 
63

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1,470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1961
65.4%
2446
30.3%
063
 
4.3%

Length

2025-10-06T17:39:05.517819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T17:39:05.572059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1961
65.4%
2446
30.3%
063
 
4.3%

Most occurring characters

ValueCountFrequency (%)
1961
65.4%
2446
30.3%
063
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1961
65.4%
2446
30.3%
063
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1961
65.4%
2446
30.3%
063
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1961
65.4%
2446
30.3%
063
 
4.3%

DistanceFromHome
Real number (ℝ)

Distinct29
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.192517
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-10-06T17:39:05.627446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.1068644
Coefficient of variation (CV)0.88189823
Kurtosis-0.2248334
Mean9.192517
Median Absolute Deviation (MAD)5
Skewness0.958118
Sum13513
Variance65.721251
MonotonicityNot monotonic
2025-10-06T17:39:05.689538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2211
14.4%
1208
14.1%
1086
 
5.9%
985
 
5.8%
384
 
5.7%
784
 
5.7%
880
 
5.4%
565
 
4.4%
464
 
4.4%
659
 
4.0%
Other values (19)444
30.2%
ValueCountFrequency (%)
1208
14.1%
2211
14.4%
384
 
5.7%
464
 
4.4%
565
 
4.4%
659
 
4.0%
784
 
5.7%
880
 
5.4%
985
5.8%
1086
5.9%
ValueCountFrequency (%)
2927
1.8%
2823
1.6%
2712
0.8%
2625
1.7%
2525
1.7%
2428
1.9%
2327
1.8%
2219
1.3%
2118
1.2%
2025
1.7%

Education
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
572 
4
398 
2
282 
1
170 
5
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1,470
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Length

2025-10-06T17:39:05.756539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T17:39:05.814611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Most occurring characters

ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

EducationField
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.247619
Minimum0
Maximum5
Zeros27
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2025-10-06T17:39:05.873071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3313691
Coefficient of variation (CV)0.59234642
Kurtosis-0.68808083
Mean2.247619
Median Absolute Deviation (MAD)1
Skewness0.55037125
Sum3304
Variance1.7725437
MonotonicityNot monotonic
2025-10-06T17:39:05.929578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1606
41.2%
3464
31.6%
2159
 
10.8%
5132
 
9.0%
482
 
5.6%
027
 
1.8%
ValueCountFrequency (%)
027
 
1.8%
1606
41.2%
2159
 
10.8%
3464
31.6%
482
 
5.6%
5132
 
9.0%
ValueCountFrequency (%)
5132
 
9.0%
482
 
5.6%
3464
31.6%
2159
 
10.8%
1606
41.2%
027
 
1.8%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
453 
4
446 
2
287 
1
284 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1,470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row4
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Length

2025-10-06T17:39:05.990471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T17:39:06.060041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Most occurring characters

ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
882 
0
588 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1,470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1882
60.0%
0588
40.0%

Length

2025-10-06T17:39:06.122815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T17:39:06.174898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1882
60.0%
0588
40.0%

Most occurring characters

ValueCountFrequency (%)
1882
60.0%
0588
40.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1882
60.0%
0588
40.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1882
60.0%
0588
40.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1882
60.0%
0588
40.0%

JobInvolvement
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
868 
2
375 
4
144 
1
 
83

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1,470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Length

2025-10-06T17:39:06.233498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T17:39:06.296909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Most occurring characters

ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

JobLevel
Categorical

High correlation 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
543 
2
534 
3
218 
4
106 
5
69 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1,470
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%

Length

2025-10-06T17:39:06.360773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T17:39:06.418646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%

Most occurring characters

ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%

JobRole
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4585034
Minimum0
Maximum8
Zeros131
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2025-10-06T17:39:06.479240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.4618213
Coefficient of variation (CV)0.55216315
Kurtosis-1.1927348
Mean4.4585034
Median Absolute Deviation (MAD)2
Skewness-0.35726992
Sum6554
Variance6.0605641
MonotonicityNot monotonic
2025-10-06T17:39:06.539364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
7326
22.2%
6292
19.9%
2259
17.6%
4145
9.9%
0131
8.9%
3102
 
6.9%
883
 
5.6%
580
 
5.4%
152
 
3.5%
ValueCountFrequency (%)
0131
8.9%
152
 
3.5%
2259
17.6%
3102
 
6.9%
4145
9.9%
580
 
5.4%
6292
19.9%
7326
22.2%
883
 
5.6%
ValueCountFrequency (%)
883
 
5.6%
7326
22.2%
6292
19.9%
580
 
5.4%
4145
9.9%
3102
 
6.9%
2259
17.6%
152
 
3.5%
0131
8.9%

JobSatisfaction
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
4
459 
3
442 
1
289 
2
280 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1,470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Length

2025-10-06T17:39:06.607250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T17:39:06.664740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Most occurring characters

ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

MaritalStatus
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
673 
2
470 
0
327 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1,470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1673
45.8%
2470
32.0%
0327
22.2%

Length

2025-10-06T17:39:06.732852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T17:39:06.787809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1673
45.8%
2470
32.0%
0327
22.2%

Most occurring characters

ValueCountFrequency (%)
1673
45.8%
2470
32.0%
0327
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1673
45.8%
2470
32.0%
0327
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1673
45.8%
2470
32.0%
0327
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1673
45.8%
2470
32.0%
0327
22.2%

MonthlyIncome
Real number (ℝ)

High correlation 

Distinct1349
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6502.9313
Minimum1009
Maximum19999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-10-06T17:39:06.852465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2097.9
Q12911
median4919
Q38379
95-th percentile17821.35
Maximum19999
Range18990
Interquartile range (IQR)5468

Descriptive statistics

Standard deviation4707.9568
Coefficient of variation (CV)0.72397455
Kurtosis1.0052327
Mean6502.9313
Median Absolute Deviation (MAD)2199
Skewness1.3698167
Sum9559309
Variance22164857
MonotonicityNot monotonic
2025-10-06T17:39:06.929687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23424
 
0.3%
61423
 
0.2%
27413
 
0.2%
25593
 
0.2%
26103
 
0.2%
24513
 
0.2%
55623
 
0.2%
34523
 
0.2%
23803
 
0.2%
63473
 
0.2%
Other values (1339)1439
97.9%
ValueCountFrequency (%)
10091
0.1%
10511
0.1%
10521
0.1%
10811
0.1%
10911
0.1%
11021
0.1%
11181
0.1%
11291
0.1%
12001
0.1%
12231
0.1%
ValueCountFrequency (%)
199991
0.1%
199731
0.1%
199431
0.1%
199261
0.1%
198591
0.1%
198471
0.1%
198451
0.1%
198331
0.1%
197401
0.1%
197171
0.1%

NumCompaniesWorked
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6931973
Minimum0
Maximum9
Zeros197
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-10-06T17:39:06.996458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.498009
Coefficient of variation (CV)0.92752545
Kurtosis0.010213817
Mean2.6931973
Median Absolute Deviation (MAD)1
Skewness1.0264711
Sum3959
Variance6.240049
MonotonicityNot monotonic
2025-10-06T17:39:07.049054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1521
35.4%
0197
 
13.4%
3159
 
10.8%
2146
 
9.9%
4139
 
9.5%
774
 
5.0%
670
 
4.8%
563
 
4.3%
952
 
3.5%
849
 
3.3%
ValueCountFrequency (%)
0197
 
13.4%
1521
35.4%
2146
 
9.9%
3159
 
10.8%
4139
 
9.5%
563
 
4.3%
670
 
4.8%
774
 
5.0%
849
 
3.3%
952
 
3.5%
ValueCountFrequency (%)
952
 
3.5%
849
 
3.3%
774
 
5.0%
670
 
4.8%
563
 
4.3%
4139
 
9.5%
3159
 
10.8%
2146
 
9.9%
1521
35.4%
0197
 
13.4%

OverTime
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1054 
1
416 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1,470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
01054
71.7%
1416
 
28.3%

Length

2025-10-06T17:39:07.108469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T17:39:07.162856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
01054
71.7%
1416
 
28.3%

Most occurring characters

ValueCountFrequency (%)
01054
71.7%
1416
 
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01054
71.7%
1416
 
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01054
71.7%
1416
 
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01054
71.7%
1416
 
28.3%

PercentSalaryHike
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.209524
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-10-06T17:39:07.209660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6599377
Coefficient of variation (CV)0.2406346
Kurtosis-0.30059822
Mean15.209524
Median Absolute Deviation (MAD)2
Skewness0.82112798
Sum22358
Variance13.395144
MonotonicityNot monotonic
2025-10-06T17:39:07.268852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
11210
14.3%
13209
14.2%
14201
13.7%
12198
13.5%
15101
6.9%
1889
6.1%
1782
 
5.6%
1678
 
5.3%
1976
 
5.2%
2256
 
3.8%
Other values (5)170
11.6%
ValueCountFrequency (%)
11210
14.3%
12198
13.5%
13209
14.2%
14201
13.7%
15101
6.9%
1678
 
5.3%
1782
 
5.6%
1889
6.1%
1976
 
5.2%
2055
 
3.7%
ValueCountFrequency (%)
2518
 
1.2%
2421
 
1.4%
2328
 
1.9%
2256
3.8%
2148
3.3%
2055
3.7%
1976
5.2%
1889
6.1%
1782
5.6%
1678
5.3%

PerformanceRating
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
1244 
4
226 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1,470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%

Length

2025-10-06T17:39:07.333509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T17:39:07.388256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%

Most occurring characters

ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
459 
4
432 
2
303 
1
276 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1,470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row2
4th row3
5th row4

Common Values

ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

Length

2025-10-06T17:39:07.445688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T17:39:07.504156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

Most occurring characters

ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

TotalWorkingYears
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.279592
Minimum0
Maximum40
Zeros11
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-10-06T17:39:07.569695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.7807817
Coefficient of variation (CV)0.68981057
Kurtosis0.91826954
Mean11.279592
Median Absolute Deviation (MAD)4
Skewness1.1171719
Sum16581
Variance60.540563
MonotonicityNot monotonic
2025-10-06T17:39:07.645029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10202
 
13.7%
6125
 
8.5%
8103
 
7.0%
996
 
6.5%
588
 
6.0%
781
 
5.5%
181
 
5.5%
463
 
4.3%
1248
 
3.3%
342
 
2.9%
Other values (30)541
36.8%
ValueCountFrequency (%)
011
 
0.7%
181
5.5%
231
 
2.1%
342
 
2.9%
463
4.3%
588
6.0%
6125
8.5%
781
5.5%
8103
7.0%
996
6.5%
ValueCountFrequency (%)
402
 
0.1%
381
 
0.1%
374
0.3%
366
0.4%
353
 
0.2%
345
0.3%
337
0.5%
329
0.6%
319
0.6%
307
0.5%

TrainingTimesLastYear
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7993197
Minimum0
Maximum6
Zeros54
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-10-06T17:39:07.705101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2892706
Coefficient of variation (CV)0.46056569
Kurtosis0.49499299
Mean2.7993197
Median Absolute Deviation (MAD)1
Skewness0.55312417
Sum4115
Variance1.6622187
MonotonicityNot monotonic
2025-10-06T17:39:07.762652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2547
37.2%
3491
33.4%
4123
 
8.4%
5119
 
8.1%
171
 
4.8%
665
 
4.4%
054
 
3.7%
ValueCountFrequency (%)
054
 
3.7%
171
 
4.8%
2547
37.2%
3491
33.4%
4123
 
8.4%
5119
 
8.1%
665
 
4.4%
ValueCountFrequency (%)
665
 
4.4%
5119
 
8.1%
4123
 
8.4%
3491
33.4%
2547
37.2%
171
 
4.8%
054
 
3.7%

WorkLifeBalance
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
893 
2
344 
4
153 
1
 
80

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1,470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

Length

2025-10-06T17:39:07.823704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T17:39:07.882735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

Most occurring characters

ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

YearsAtCompany
Real number (ℝ)

High correlation  Zeros 

Distinct37
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0081633
Minimum0
Maximum40
Zeros44
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-10-06T17:39:07.946622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.1265252
Coefficient of variation (CV)0.87419841
Kurtosis3.9355088
Mean7.0081633
Median Absolute Deviation (MAD)3
Skewness1.7645295
Sum10302
Variance37.53431
MonotonicityNot monotonic
2025-10-06T17:39:08.021694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5196
13.3%
1171
11.6%
3128
8.7%
2127
8.6%
10120
8.2%
4110
 
7.5%
790
 
6.1%
982
 
5.6%
880
 
5.4%
676
 
5.2%
Other values (27)290
19.7%
ValueCountFrequency (%)
044
 
3.0%
1171
11.6%
2127
8.6%
3128
8.7%
4110
7.5%
5196
13.3%
676
 
5.2%
790
6.1%
880
5.4%
982
5.6%
ValueCountFrequency (%)
401
 
0.1%
371
 
0.1%
362
 
0.1%
341
 
0.1%
335
0.3%
323
0.2%
313
0.2%
301
 
0.1%
292
 
0.1%
272
 
0.1%

YearsInCurrentRole
Real number (ℝ)

High correlation  Zeros 

Distinct19
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2292517
Minimum0
Maximum18
Zeros244
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-10-06T17:39:08.085601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.623137
Coefficient of variation (CV)0.85668513
Kurtosis0.47742077
Mean4.2292517
Median Absolute Deviation (MAD)3
Skewness0.91736316
Sum6217
Variance13.127122
MonotonicityNot monotonic
2025-10-06T17:39:08.150316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2372
25.3%
0244
16.6%
7222
15.1%
3135
 
9.2%
4104
 
7.1%
889
 
6.1%
967
 
4.6%
157
 
3.9%
637
 
2.5%
536
 
2.4%
Other values (9)107
 
7.3%
ValueCountFrequency (%)
0244
16.6%
157
 
3.9%
2372
25.3%
3135
 
9.2%
4104
 
7.1%
536
 
2.4%
637
 
2.5%
7222
15.1%
889
 
6.1%
967
 
4.6%
ValueCountFrequency (%)
182
 
0.1%
174
 
0.3%
167
 
0.5%
158
 
0.5%
1411
 
0.7%
1314
 
1.0%
1210
 
0.7%
1122
 
1.5%
1029
2.0%
967
4.6%

YearsSinceLastPromotion
Real number (ℝ)

High correlation  Zeros 

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1877551
Minimum0
Maximum15
Zeros581
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-10-06T17:39:08.212774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.2224303
Coefficient of variation (CV)1.4729392
Kurtosis3.6126731
Mean2.1877551
Median Absolute Deviation (MAD)1
Skewness1.98429
Sum3216
Variance10.384057
MonotonicityNot monotonic
2025-10-06T17:39:08.275066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0581
39.5%
1357
24.3%
2159
 
10.8%
776
 
5.2%
461
 
4.1%
352
 
3.5%
545
 
3.1%
632
 
2.2%
1124
 
1.6%
818
 
1.2%
Other values (6)65
 
4.4%
ValueCountFrequency (%)
0581
39.5%
1357
24.3%
2159
 
10.8%
352
 
3.5%
461
 
4.1%
545
 
3.1%
632
 
2.2%
776
 
5.2%
818
 
1.2%
917
 
1.2%
ValueCountFrequency (%)
1513
 
0.9%
149
 
0.6%
1310
 
0.7%
1210
 
0.7%
1124
 
1.6%
106
 
0.4%
917
 
1.2%
818
 
1.2%
776
5.2%
632
2.2%

YearsWithCurrManager
Real number (ℝ)

High correlation  Zeros 

Distinct18
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1231293
Minimum0
Maximum17
Zeros263
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-10-06T17:39:08.338406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5681361
Coefficient of variation (CV)0.86539517
Kurtosis0.17105808
Mean4.1231293
Median Absolute Deviation (MAD)3
Skewness0.83345099
Sum6061
Variance12.731595
MonotonicityNot monotonic
2025-10-06T17:39:08.519859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2344
23.4%
0263
17.9%
7216
14.7%
3142
9.7%
8107
 
7.3%
498
 
6.7%
176
 
5.2%
964
 
4.4%
531
 
2.1%
629
 
2.0%
Other values (8)100
 
6.8%
ValueCountFrequency (%)
0263
17.9%
176
 
5.2%
2344
23.4%
3142
9.7%
498
 
6.7%
531
 
2.1%
629
 
2.0%
7216
14.7%
8107
 
7.3%
964
 
4.4%
ValueCountFrequency (%)
177
 
0.5%
162
 
0.1%
155
 
0.3%
145
 
0.3%
1314
 
1.0%
1218
 
1.2%
1122
 
1.5%
1027
 
1.8%
964
4.4%
8107
7.3%

PTOs Utilized
Real number (ℝ)

Zeros 

Distinct20
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4680272
Minimum0
Maximum19
Zeros70
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-10-06T17:39:08.581972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median9
Q314
95-th percentile19
Maximum19
Range19
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.7735811
Coefficient of variation (CV)0.60979769
Kurtosis-1.1747672
Mean9.4680272
Median Absolute Deviation (MAD)5
Skewness0.036623833
Sum13918
Variance33.334239
MonotonicityNot monotonic
2025-10-06T17:39:08.643966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1994
 
6.4%
282
 
5.6%
1080
 
5.4%
879
 
5.4%
1278
 
5.3%
1377
 
5.2%
677
 
5.2%
1175
 
5.1%
474
 
5.0%
973
 
5.0%
Other values (10)681
46.3%
ValueCountFrequency (%)
070
4.8%
172
4.9%
282
5.6%
373
5.0%
474
5.0%
568
4.6%
677
5.2%
771
4.8%
879
5.4%
973
5.0%
ValueCountFrequency (%)
1994
6.4%
1864
4.4%
1769
4.7%
1659
4.0%
1572
4.9%
1463
4.3%
1377
5.2%
1278
5.3%
1175
5.1%
1080
5.4%

Interactions

2025-10-06T17:39:03.820409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:53.192904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:53.951362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:54.731179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:55.576234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:56.374376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:57.186900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:57.950074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:58.845219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:59.668225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:00.451214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:01.280608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:02.199904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:03.004313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:03.872909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:53.243109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:54.003075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:54.780710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:55.626925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:56.426630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:57.234356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:58.002224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:58.894872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:59.720305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:00.502254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:01.329724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:02.250239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:03.057096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:03.927668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:53.295276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:54.056257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:54.834200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:55.683310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:56.484196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:57.287915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:58.057224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:58.949152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:59.777900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:00.571115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:01.386805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:02.307987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:03.114886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:03.979443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:53.347421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:54.110531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:54.882766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:55.736183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:56.538932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:57.339311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:58.111526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:59.005823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:59.831126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:00.626739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:01.440311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:02.359730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:03.169863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:04.039581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:53.404635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:54.165845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:54.938609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:55.794490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:56.598971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:57.396219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:58.170838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:59.066771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:59.887793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:00.686596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:01.508612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:02.416975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:03.229298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:04.100134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:53.459086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:54.225031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:54.995190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:55.851770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:56.656937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:57.450075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:58.229231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:59.128267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:59.945514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:00.747273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:01.565964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:02.475183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:03.290497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:04.153738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:53.507051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:54.276302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:55.045241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:55.904731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:56.709408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:57.497027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:58.281412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:59.182336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:59.995217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:00.801459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:01.620712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:02.531530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:03.344768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:04.208045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:53.558917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:54.330369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:55.098228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:55.960805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:56.766791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:57.551250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:58.335253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:59.241093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:00.050425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:00.860559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:01.682955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:02.587971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:03.403948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:04.268748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:53.618563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:54.387799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:55.154841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:56.020630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:56.828516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:57.605886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:58.399000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:59.303202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:00.108440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:00.921409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:01.744122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:02.649213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:03.463853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:04.321906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:53.671791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:54.443552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:55.207123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:56.076059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:56.884213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:57.662770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:58.453401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:59.362718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:00.163793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:00.976448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:01.904947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:02.705442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:03.520817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:04.385668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:53.729397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:54.499983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:55.265001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:56.136222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:56.947370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:57.721193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:58.610532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:59.422505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:00.223042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:01.036654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:01.965904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:02.767814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:03.582252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:04.444924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:53.784413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:54.559115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:55.320055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:56.196013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:57.007050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:57.776199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:58.666865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:59.483423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:00.279459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:01.094869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:02.024466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:02.828354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:03.642296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:04.506174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:53.840083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:54.616570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:55.375521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:56.255225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:57.067540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:57.838510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:58.726271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:59.543099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:00.335871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:01.152391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:02.083813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:02.886808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:03.702815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:04.564936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:53.896963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:54.675167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:55.520179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:56.314297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:57.126044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:57.895796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:58.786290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:38:59.605255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:00.393871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:01.210825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:02.141772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:02.945808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-06T17:39:03.763408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-10-06T17:39:08.713796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AgeAttritionBusinessTravelDepartmentDistanceFromHomeEducationEducationFieldEnvironmentSatisfactionGenderJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeNumCompaniesWorkedOverTimePTOs UtilizedPercentSalaryHikePerformanceRatingRelationshipSatisfactionTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
Age1.0000.2130.0410.000-0.0190.153-0.0450.0060.0000.0250.295-0.1280.0000.1410.4720.3530.000-0.0160.0080.0000.0350.6570.0000.0330.2520.1980.1740.195
Attrition0.2131.0000.1230.0770.0670.0000.0870.1150.0090.1320.2160.2310.0990.1730.2170.1070.2430.0000.0000.0000.0390.2080.0790.0950.1730.1690.0270.179
BusinessTravel0.0410.1231.0000.0000.0230.0000.0000.0000.0370.0160.0000.0000.0000.0350.0250.0000.0240.0000.0300.0000.0000.0000.0000.0000.0000.0000.0300.064
Department0.0000.0770.0001.0000.0000.0000.5880.0180.0260.0000.2120.9370.0290.0300.1870.0320.0000.0390.0000.0000.0200.0240.0000.0470.0000.0000.0000.000
DistanceFromHome-0.0190.0670.0230.0001.0000.0000.0170.0000.0300.0280.0540.0160.0000.0000.003-0.0100.0660.0050.0300.0580.025-0.003-0.0250.0000.0110.014-0.0050.004
Education0.1530.0000.0000.0000.0001.0000.0550.0190.0000.0000.0880.0510.0150.0000.0940.1010.0010.0330.0210.0000.0160.0950.0270.0000.0710.0290.0000.000
EducationField-0.0450.0870.0000.5880.0170.0551.0000.0310.0000.0000.0910.0190.0170.000-0.035-0.0120.0000.021-0.0020.0000.040-0.0220.0490.027-0.001-0.0070.0130.008
EnvironmentSatisfaction0.0060.1150.0000.0180.0000.0190.0311.0000.0000.0340.0000.0000.0000.0190.0000.0000.0600.0470.0000.0000.0000.0000.0000.0000.0310.0360.0000.000
Gender0.0000.0090.0370.0260.0300.0000.0000.0001.0000.0000.0480.0740.0000.0320.0460.0000.0310.0000.0490.0000.0000.0000.0000.0000.0660.0790.0000.000
JobInvolvement0.0250.1320.0160.0000.0280.0000.0000.0340.0001.0000.0000.0000.0000.0240.0460.0000.0000.0570.0360.0000.0000.0000.0130.0000.0530.0000.0000.044
JobLevel0.2950.2160.0000.2120.0540.0880.0910.0000.0480.0001.0000.5690.0000.0460.8640.1130.0000.0340.0000.0000.0000.5390.0170.0000.3530.2410.2060.232
JobRole-0.1280.2310.0000.9370.0160.0510.0190.0000.0740.0000.5691.0000.0000.061-0.044-0.0660.000-0.0070.0020.0000.030-0.1480.0220.029-0.055-0.013-0.019-0.035
JobSatisfaction0.0000.0990.0000.0290.0000.0150.0170.0000.0000.0000.0000.0001.0000.0000.0000.0000.0220.0480.0000.0260.0000.0240.0210.0000.0000.0000.0000.000
MaritalStatus0.1410.1730.0350.0300.0000.0000.0000.0190.0320.0240.0460.0610.0001.0000.0610.0380.0000.0000.0000.0000.0250.0690.0000.0000.0000.0400.0350.000
MonthlyIncome0.4720.2170.0250.1870.0030.094-0.0350.0000.0460.0460.864-0.0440.0000.0611.0000.1900.000-0.028-0.0340.0000.0430.710-0.0350.0000.4640.3950.2650.365
NumCompaniesWorked0.3530.1070.0000.032-0.0100.101-0.0120.0000.0000.0000.113-0.0660.0000.0380.1901.0000.0000.0130.0000.0000.0000.315-0.0470.051-0.171-0.128-0.067-0.144
OverTime0.0000.2430.0240.0000.0660.0010.0000.0600.0310.0000.0000.0000.0220.0000.0000.0001.0000.0350.0000.0000.0250.0000.0990.0000.0180.0420.0110.000
PTOs Utilized-0.0160.0000.0000.0390.0050.0330.0210.0470.0000.0570.034-0.0070.0480.000-0.0280.0130.0351.0000.0070.0380.015-0.014-0.0020.0000.0010.0290.0050.008
PercentSalaryHike0.0080.0000.0300.0000.0300.021-0.0020.0000.0490.0360.0000.0020.0000.000-0.0340.0000.0000.0071.0000.9970.027-0.026-0.0040.000-0.054-0.026-0.055-0.026
PerformanceRating0.0000.0000.0000.0000.0580.0000.0000.0000.0000.0000.0000.0000.0260.0000.0000.0000.0000.0380.9971.0000.0000.0000.0000.0000.0000.0310.0000.030
RelationshipSatisfaction0.0350.0390.0000.0200.0250.0160.0400.0000.0000.0000.0000.0300.0000.0250.0430.0000.0250.0150.0270.0001.0000.0310.0000.0000.0000.0000.0500.000
TotalWorkingYears0.6570.2080.0000.024-0.0030.095-0.0220.0000.0000.0000.539-0.1480.0240.0690.7100.3150.000-0.014-0.0260.0000.0311.000-0.0140.0000.5940.4930.3350.495
TrainingTimesLastYear0.0000.0790.0000.000-0.0250.0270.0490.0000.0000.0130.0170.0220.0210.000-0.035-0.0470.099-0.002-0.0040.0000.000-0.0141.0000.0000.0010.0050.010-0.012
WorkLifeBalance0.0330.0950.0000.0470.0000.0000.0270.0000.0000.0000.0000.0290.0000.0000.0000.0510.0000.0000.0000.0000.0000.0000.0001.0000.0200.0250.0000.031
YearsAtCompany0.2520.1730.0000.0000.0110.071-0.0010.0310.0660.0530.353-0.0550.0000.0000.464-0.1710.0180.001-0.0540.0000.0000.5940.0010.0201.0000.8540.5200.843
YearsInCurrentRole0.1980.1690.0000.0000.0140.029-0.0070.0360.0790.0000.241-0.0130.0000.0400.395-0.1280.0420.029-0.0260.0310.0000.4930.0050.0250.8541.0000.5060.725
YearsSinceLastPromotion0.1740.0270.0300.000-0.0050.0000.0130.0000.0000.0000.206-0.0190.0000.0350.265-0.0670.0110.005-0.0550.0000.0500.3350.0100.0000.5200.5061.0000.467
YearsWithCurrManager0.1950.1790.0640.0000.0040.0000.0080.0000.0000.0440.232-0.0350.0000.0000.365-0.1440.0000.008-0.0260.0300.0000.495-0.0120.0310.8430.7250.4671.000

Missing values

2025-10-06T17:39:04.665670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-06T17:39:04.866970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeAttritionBusinessTravelDepartmentDistanceFromHomeEducationEducationFieldEnvironmentSatisfactionGenderJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeNumCompaniesWorkedOverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerPTOs Utilized
04112212120327425993811131801640516
1490118113122621513010234410331071710
2371212244121232209061153273300001
3330113414031631290911113383387302
42702121311312213468901234633222211
53201122141312423068001333822773611
659021333304121126704120411232100016
73002124114131230269310224212310009
838011233141234329526002142102397184
9360212733313203152376013321732777712
AgeAttritionBusinessTravelDepartmentDistanceFromHomeEducationEducationFieldEnvironmentSatisfactionGenderJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeNumCompaniesWorkedOverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerPTOs Utilized
146029021284340216123785101432531540416
1461501222832412371010854411332203332201
14623902224122024741120310011312122209964
146331001533213241299360019321023941718
14642602253440218322966001834523420011
146536011232331422412571401733173352031
1466390216134123011999140153195377171
1467270214312142421614211204260362031
14684901223341227215390201434173296081
14693402183321422314404201231634431214